Metis: Selecting Diverse Atlas Vantage Points
Bibliographic record
Abstract
The popularity of the RIPE Atlas measurement platform comes primarily from its openness and unprecedented scale. The platform provides users with over ten thousand vantage points, called probes, and is usually considered as giving a reasonably faithful view of the Internet. A good use of Atlas, however, requires a clear understanding of its limitations and bias. In this work we highlight the influence of probe locations on Atlas measurements and advocate the importance of selecting a diverse set of probes for fair measurements. We propose Metis, a data-driven probe selection method, that picks a diverse set of probes based on topological properties (e.g., round-trip time or AS-path length). Using real experiments we show that, compared to Atlas’ default probe selection, Metis’ probe selections collect more comprehensive measurement results in terms of geographical, topological, RIR, and industry-type coverage. Metis triples the number of probes from the underrepresented AFRINIC and LACNIC regions, and improves geographical diversity by increasing the number of unique countries included in the probe set by up to 59%. In addition, we extend Metis to identify locations on the Internet where new probes would be the most beneficial for improving Atlas’ footprint. Finally, we present a website where we publish periodically updated results and provide easy integration of Metis’ selections with Atlas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".